Computational Methods

Reverse-engineering regulatory networks

Finding the genetic mechanisms responsible for a given set of resultant phenotypes remains a significant challenge due to both the non-linearity of regulatory networks and the difficulty of testing spatial-temporal models. We investigate computational systems using novel algorithms in machine learning and high-performance computing that can automatically reverse-engineer systems-level dynamic regulatory networks, including all the necessary genes, regulatory interactions, and kinetic parameters, directly from an input dataset of experimental perturbations and their resulting outcomes. Our approach is general enough to be applicable to a diverse set of problems, including complex non-linear signaling networks, genetic and pharmacological experimental perturbations, and spatial-temporal morphological outcomes, paving the way towards automated machine scientists to help us in understanding complex biological regulation.

High performance in silico experiments

To streamline the discovery of much sought-after systems-level regulatory networks, we develop high-performance simulators capable of performing the same temporal and spatial experiments done at the bench, including genetic knock-downs, pharmacological treatments, and surgical manipulations. In this way, fast in silico experiments can be performed by both human scientists and artificial intelligence machines to discover testable, complete genetic networks that recapitulates the same resultant phenotypes from the experiments in vivo.

Automatic hypothesis generation

In addition to automatically inferring models from experimental data, we investigate software systems that can also generate novel hypothesis and testable predictions. These systems can automatically discover novel components missing from the input experimental dataset but detected as necessary to explain the resultant phenotypes. Using this approach, we can discover new genes and regulatory interactions and suggest the specific experiments neccesary to validate the new hypothesis at the bench.